System And Method For Effectively Performing An Image Categorization Procedure

ABSTRACT

A system for performing an image categorization procedure includes an image manager with a keypoint generator, a support region filter, an orientation filter, and a matching module. The keypoint generator computes initial descriptors for keypoints in a test image. The support region filter and the orientation filter perform respective filtering procedures upon the initial descriptors to produce filtered descriptors. The matching module compares the filtered descriptors to one or more database image sets for categorizing said test image. A processor of an electronic device typically controls the image manager to effectively perform the image categorization procedure.

BACKGROUND SECTION

1. Field of the Invention

This invention relates generally to techniques for managing imageinformation, and relates more particularly to a system and method foreffectively performing an image categorization procedure.

2. Description of the Background Art

Implementing effective methods for managing image information is asignificant consideration for designers and manufacturers of electronicsystems. However, effectively implementing these electronic systems maycreate substantial challenges for system designers. For example,enhanced demands for increased system functionality and performance mayrequire more system processing power and require additional hardwareresources. An increase in processing or hardware requirements may alsoresult in a corresponding detrimental economic impact due to increasedproduction costs and operational inefficiencies.

Furthermore, enhanced system capability to perform various advancedoperations may provide additional benefits to a system user, but mayalso place increased demands on the control and management of varioussystem components. For example, an enhanced electronic system thateffectively supports image categorization procedures may benefit from anefficient implementation because of the large amount and complexity ofthe digital data involved.

Due to growing demands on system resources and substantially increasingdata magnitudes, it is apparent that developing new techniques formanaging image information is a matter of concern for related electronictechnologies. Therefore, for all the foregoing reasons, developingeffective systems for managing image information remains a significantconsideration for designers, manufacturers, and users of contemporaryelectronic systems.

SUMMARY

In accordance with the present invention, a system and method aredisclosed for effectively performing an image categorization procedure.In accordance with one embodiment of the present invention, an imagemanager or other appropriate entity initially selects at least one testimage for performing the image categorization procedure. The imagemanager then performs a down-sampling process upon the test image toproduce a down-sampled image that typically has a lower resolution andfewer pixels than the original test image. In certain embodiments, theimage manager may also perform a spectral filtering process upon thedown-sampled image to produce a filtered image with a predeterminedamount of the lower frequencies removed.

The image manager utilizes a keypoint generator to compute keypointlocations, support regions, and local orientations that are utilized todefine initial descriptors for the keypoints in the particular testimage. In certain embodiments, a keypoint descriptor may be implementedas a multi-dimensional descriptor (e.g., 128 dimensions) for a specifiedradius (support region) around a given keypoint. The image manager thenutilizes a support region filter to perform a support region filteringprocedure. For example, in certain embodiments, the support regionfilter may accept only support regions that are larger than a predefinedsupport-region size threshold. In addition, the support region filtermay reject any highly-overlapped support regions during the supportregion filtering procedure.

The image manager also may utilize an orientation filter to perform anorientation filtering procedure by utilizing any effective techniques.In certain embodiments, the orientation filter identifies any outlierkeypoint pairs with orientations that differ significantly from theother keypoint pairs. These outliers may then be removed from furtherconsideration during the orientation filtering procedure.

Following the foregoing support region filtering procedure andorientation filtering procedure, the image manager provides theresultant filtered descriptors to a matching module which performs adescriptor matching procedure to compare the filtered descriptors for agiven test image with images from various previously-categorized imagesets stored in an image database.

The matching module may utilize any effective and robust matchingtechniques for performing the descriptor matching procedure. Forexample, in certain embodiments, matching module may compare thefiltered descriptors from the test image with corresponding descriptorsfrom various image sets in the categorized image database. The matchingmodule may identify an image matching result between the test image andthe database image if the number of matching descriptors is greater thana predefined match threshold.

The matching module may also perform a match filtering procedure toreject, false positive matches by requiring a low matching distance thatis less than a predefined distance threshold. After the match filteringprocedure is completed, then the resultant categorized image may beadded to the appropriate image set in the database to complete the imagecategorization procedure. For all the foregoing reasons, the presentinvention therefore provides an improved a system and method foreffectively performing an image categorization procedure

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an electronic system, in accordance withone embodiment of the present invention;

FIG. 2 is a block diagram for one embodiment of the computer of FIG. 1,in accordance with the present invention;

FIG. 3 is a block diagram for one embodiment of the memory of FIG. 2, inaccordance with the present invention;

FIG. 4 is a block diagram of the image manager of FIG. 3, in accordancewith one embodiment of the present invention;

FIG. 5 is a block diagram of an image database, in accordance with oneembodiment of the present invention;

FIG. 6 is a flowchart illustrating a general approach for performing animage categorization procedure, in accordance with one embodiment of thepresent invention;

FIG. 7 is a diagram illustrating an image categorization procedure, inaccordance with one embodiment of the present invention;

FIG. 8 is a diagram illustrating an exemplary keypoint and supportregion, in accordance with one embodiment of the present invention; and

FIGS. 9A and 9B are diagrams illustrating an orientation filteringprocedure, in accordance with one embodiment of the present invention.

DETAILED DESCRIPTION

The present invention relates to an improvement in image categorizationsystems. The following description is presented to enable one ofordinary skill in the art to make and use the invention, and is providedin the context of a patent application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the generic principles herein may beapplied to other embodiments. Thus, the present invention is notintended to be limited to the embodiments shown, but is to be accordedthe widest scope consistent with the principles and features describedherein.

The present invention is described herein as a system and method foreffectively performing an image categorization procedure, and mayinclude an image manager with a keypoint generator, a support regionfilter, an orientation filter, and a matching module. The keypointgenerator computes initial descriptors for keypoints in a test image.The support region filter and the orientation filter perform respectivefiltering procedures upon the initial descriptors to produce filtereddescriptors. The matching module compares the filtered descriptors toone or more database image sets for categorizing the test image. Aprocessor of an electronic device typically controls the image managerto effectively perform the image categorization procedure.

Referring now to FIG. 1, a block diagram of an electronic system 110 isshown, in accordance with one embodiment of the present invention. Inthe FIG. 1 embodiment, electronic system 110 may include, but is notlimited to, a computer 112 and a network 114. In alternate embodiments,electronic system 110 may be implemented using various components andconfigurations in addition to, or instead of those discussed inconjunction with the FIG. 1 embodiment. For example, system 110 mayreadily include any number of other electronic devices in addition tocomputer 112.

In the FIG. 1 embodiment, computer 112 may be implemented as anyelectronic device that is configured to support and manage variousfunctionalities in electronic system 110. In the FIG. 1 embodiment,network 114 may include any appropriate entities or devices thatcommunicate with computer 112 via wireless or wired communicationtechniques. In the FIG. 1 embodiment, computer 112 may perform an imagecategorization procedure.

In accordance with the present invention, computer 112 is designed toidentify images that are captured at the same location in a short periodof time so that the contents are highly correlated. The images are verysimilar with a high proportion of content overlapping and limitedchanges in characteristics such as composition, content, exposure, andcamera settings like zoom, shift, and rotation. The general method forthis invention is to use content-based image retrieval in which a set ofsimilarity measures or descriptors are generated to measure how imagesare correlated. This may result in false matches that include imagesthat are similar according to the designed similarity measures, but arequite different to human perception. A second stage of the invention maytherefore advantageously filter the descriptors to remove the unwantedfalse matches.

In certain embodiments, a keypoint generator identifies potentialpoint-to-point correspondences (keypoints) between images. This basicinput is further processed in a set of novel filtering procedures torecognize highly reliable correspondences. With the help of thesefilters, larger corresponding regions are identified that are reliableacross various changing conditions. New matching criteria are alsodesigned so that these reliable correspondences translate to acategorization algorithm for the whole image. A database for existingmatched images is also implemented so that each new test image may bequickly matched against previously categorized images. Theimplementation and utilization of the FIG. 1 computer 112 is furtherdiscussed below in conjunction with FIGS. 2-9B.

Referring now to FIG. 2, a block diagram for one embodiment of the FIG.1 computer 112 is shown, in accordance with the present invention. Inthe FIG. 2 embodiment, computer 112 may include, but is not limited to,a central processing unit (CPU) 212, a display 214, a memory 220, andone or more input/output interfaces (I/O interfaces) 224. In alternateembodiments, computer 112 may be implemented using various componentsand configurations in addition to, or instead of, those certain of thosecomponents and configurations discussed in conjunction with the FIG. 2embodiment. In addition, computer 112 may alternately be implemented asany other desired type of electronic device or entity.

In the FIG. 2 embodiment, CPU 212 may be implemented to include anyappropriate and compatible microprocessor device that preferablyexecutes software instructions to thereby control and manage theoperation of computer 112. The FIG. 2 display 214 may include anyeffective type of display technology including a cathode-ray-tubemonitor or a liquid-crystal display device with an appropriate screenfor displaying various information to a device user.

In the FIG. 2 embodiment, memory 220 may be implemented to include anycombination of desired storage devices, including, but not limited to,read-only memory (ROM), random-access memory (RAM), and various types ofnon-volatile memory, such as floppy disks or hard disks. The contentsand functionality of memory 220 are further discussed below inconjunction with FIG. 3.

In the FIG. 2 embodiment, I/O interfaces 224 may preferably include oneor more input and/or output interfaces to receive and/or transmit anyrequired types of information for computer 112. For example, in the FIG.2 embodiment, computer 112 may utilize I/O interfaces 224 to communicatewith network 114 (see FIG. 1). In addition, a system user may utilizeI/O interfaces 224 to communicate with computer 112 by utilizing anyappropriate and effective techniques. The implementation and utilizationof the FIG. 2 computer 112 is further discussed below in conjunctionwith FIGS. 3-9B.

Referring now to FIG. 3, a block diagram for one embodiment of the FIG.2 memory 220 is shown, in accordance with the present invention. In theFIG. 3 embodiment, memory 220 includes, but is not limited to,application software 312, an operating system 316, an image manager 320,image data 324, miscellaneous information 328, and processing data 332.In alternate embodiments, memory 220 may include various othercomponents and functionalities in addition to, or instead of, certainthose components and functionalities discussed in conjunction with theFIG. 3 embodiment.

In the FIG. 3 embodiment, application software 312 may include programinstructions that are preferably executed by CPU 212 (FIG. 2) to performvarious functions and operations for computer 112. The particular natureand functionality of application software 312 preferably variesdepending upon factors such as the specific type and particularfunctionality of the corresponding computer 112.

In the FIG. 3 embodiment, operating system 316 preferably controls andcoordinates low-level functionality of computer 112. In the FIG. 3embodiment, image manager 320 may utilize image data 324 to effectivelyperform various image categorization procedures that accuratelycharacterize and categorize a test image with respect one or more otherimages, in accordance with the present invention. In the FIG. 3embodiment, miscellaneous information 328 may include any additionalinformation or data required by computer 112 or image manager 320. Inthe FIG. 3 embodiment, processing data 332 may include any temporary orpermanent information or data required by computer 112 or image manager320 for performing image categorization procedures.

In the FIG. 3 embodiment, the present invention is disclosed anddiscussed as being implemented primarily as software. However, inalternate embodiments, some or all of the functions of the presentinvention may be performed by appropriate electronic hardware circuitsthat are configured for performing various functions that are equivalentto those functions of the software modules discussed herein. Theimplementation and utilization of image manager 320 are furtherdiscussed below in conjunction with FIGS. 4 through 9B.

Referring now to FIG. 4, a block diagram of an image manager 414 isshown, in accordance with one embodiment of the present invention. Inthe FIG. 4 embodiment, image manager 320 includes, but is not limitedto, a keypoint generator 412, a support region filter 416, anorientation filter 420, and a matching module 424. In alternateembodiments, image manager 414 may be implemented using variouscomponents and configurations in addition to, or instead of, certain ofthose components and configurations discussed in conjunction with theFIG. 4 embodiment.

In the FIG. 4 embodiment, image manager 320 may utilize keypointgenerator 412 to generate appropriate keypoints for creatingcorresponding descriptors to characterize images. In the presentinvention, keypoint generator 412 may define keypoints in an imageaccording to any appropriate criteria or techniques. In one embodiment,keypoint generator 412 may be implemented to include a conventional orenhanced version of a known Scalar Invariant Feature Transform (SIFT).

In certain embodiments, keypoint generator 412 performs a scale-spaceextrema detection procedure to derive a support region σ correspondingto a keypoint. The keypoint generator 412 also performs a keypointlocalization procedure to thereby define specific horizontal andvertical coordinates (x, y, σ) for the keypoint with respect to thesupport region. The keypoint generator 412 further performs anorientation assignment procedure to define a specific orientation θ forthe keypoint. Finally, the keypoint generator 412 utilizes the foregoinginformation to create a keypoint descriptor S(x, y, σ, θ) to representthe keypoint with respect to location, support region, and orientationwithin a given image. Additional details with respect to keypointgenerator 412 are provided below in conjunction with FIGS. 6-9B.

In the FIG. 4 embodiment, image manager 320 may utilize support regionfilter 416 to perform a support region filtering procedure that isfurther discussed below in conjunction with FIGS. 6-9B. In the FIG. 4embodiment, image manager 320 may utilize orientation filter 420 toperform an orientation filtering procedure that is further discussedbelow in conjunction with FIGS. 6-9B. In the FIG. 4 embodiment, imagemanager 320 may utilize matching module 424 to perform a matchingprocedure that is further discussed below in conjunction with FIGS.6-9B.

Referring now to FIG. 5, a block diagram of an image database 514 isshown, in accordance with one embodiment of the present invention. Inalternate embodiments, image database 514 may be implemented usingvarious components and configurations in addition to, or instead of,certain of those components and configurations discussed in conjunctionwith the FIG. 5 embodiment.

In the FIG. 5 embodiment, image database 514 includes a series ofindividual image sets 540(a) through 540(n). In accordance with certainembodiments of the present invention, each image set 540 represents adistinct category of images that has previously been identified by imagemanager 320 (FIG. 4) as being captured at the same approximate location.In the FIG. 5 embodiment, appropriate keypoints and descriptors arepreferably stored along with the categorized image to facilitate futurecategorization procedures with respect to new test images.

In accordance with various embodiments of the present invention, imagedatabase 514 may be implemented and stored in any appropriatelocation(s). For example, image database 514 may reside on computer 112,or alternately, may reside on one or more computers, databases, or otherlocations in network 114 (FIG. 1). Additional details for effectivelyperforming an image categorization procedure are further discussed belowin conjunction with FIGS. 6-9B.

Referring now to FIG. 6, a flowchart illustrating a general approach forperforming an image categorization procedure is shown, in accordancewith one embodiment of the present invention. The FIG. 6 embodiment ispresented for purposes of illustration, and in alternate embodiments,image categorization procedures may be performed using various steps andfunctionalities in addition to, or instead of, certain of those stepsand functionalities discussed in conjunction with the FIG. 6 embodiment.

In step 612 of the FIG. 6 embodiment, the present invention focuses onhigher spectral content in a given test image to eliminate variations inexposure and lighting during the image categorization procedure. In step614, a keypoint generator 412 (FIG. 4) is applied to the test image toachieve scale and orientation invariance, and to acquire informationregarding keypoints in the image. In particular, high-dimensionaldescriptors may be created that include location information, supportregions, and principle orientations for the respective keypoints.

In step 616, the present invention selects large support regions toprovide an improved characterization of local regions. In step 618, thepresent invention filters the orientation characteristics to eliminateoutliers (false matches) between pairs of keypoints. Finally, in step620, the present invention utilizes robust matching criteria to matchand verify the filtered descriptors of pairs of keypoints. Additionaldetails and techniques for performing the foregoing image categorizationprocedure are further discussed below in conjunction with FIGS. 7-9B.

Referring now to FIG. 7, a diagram illustrating an image categorizationprocedure is shown, in accordance with one embodiment of the presentinvention. The FIG. 7 embodiment is presented for purposes ofillustration, and in alternate embodiments, image categorizationprocedures may be performed using various steps and functionalities inaddition to, or instead of, certain of those steps and functionalitiesdiscussed in conjunction with the FIG. 7 embodiment.

In the FIG. 7 embodiment, an image manager 320 or other appropriateentity initially selects at least one test image 324 for performing theimage categorization procedure. In step 714, the image manager 320 thenperforms a down sampling process upon the test image 324 to produce adown-sampled image that typically has a lower resolution and fewerpixels than the original test image 324. In certain embodiments, theimage manager 320 may also perform a spectral'filtering process upon thedown-sampled image to produce a filtered image with a predeterminedamount of the lower frequencies removed.

In step 718, the image manager 320 utilizes a keypoint generator 412(FIG. 4) to compute keypoint locations 814, support regions 818, andlocal orientations 822 that are utilized to define initial descriptorsfor the keypoints in the particular test image, as discussed above inconjunction with FIG. 4. In certain embodiments, a keypoint descriptormay be implemented as a multi-dimensional descriptor (e.g., 128dimensions) for a specified radius (support region) around a givenkeypoint. In step 722, image manager 320 utilizes a support regionfilter 416 (FIG. 4) to perform a support region filtering procedure, asdiscussed further below in conjunction with FIG. 8. In step 726, imagemanager 320 utilizes, an orientation filter 420 (FIG. 4) to perform anorientation filtering procedure, as discussed further below inconjunction with FIGS. 9A-9B.

Following the foregoing support region filtering procedure andorientation filtering procedure, the image manager 320 provides theresultant filtered descriptors 730 to a matching module 424 (FIG. 4)that performs a descriptor matching procedure 734 to compare thefiltered descriptors 730 for a given test image 324 with images fromvarious previously-categorized image sets 540 from an image database 514(see FIG. 5).

Matching module 424 may utilize any effective and robust matchingtechniques for performing the descriptor matching procedure. Forexample, in certain embodiments, matching module 424 may compare thefiltered descriptors 730 for the keypoints from the test image withcorresponding keypoint descriptors from various image sets in database514. In the FIG. 7 embodiment, matching module 424 may calculate aEuclidean metric to define a matching distance d(D_(i), D_(i)′) betweencorresponding pairs of keypoints from the test image 324 and a databaseimage 540 by comparing each of the corresponding 128-D descriptors D_(i)from the test image 324 and a database image 540.

The matching module 424 may then identify a matching result between thetest image 324 and the database image 540 if the number of matchingdescriptors is greater than a predefined match threshold. In the FIG. 7embodiment, the predefined match threshold may be set at five matchingdescriptor pairs. In step 738, matching module 424 may also perform amatch filtering procedure to reject false positive matches by requiringa low matching distance d(D_(i), D_(i)′) that is less than a predefineddistance threshold. In the FIG. 7 embodiment, the predefined distancethreshold may be equal to 120,000. After the match filtering procedureis completed, then the resultant matched image 742 may be added to theappropriate image set 540 in database 514 to complete the imagecategorization procedure. Additional details and techniques forperforming the foregoing support region filtering and orientationfiltering are further discussed below in conjunction with FIGS. 8-9B.

Referring now to FIG. 8, a diagram illustrating an exemplary keypoint814 and corresponding support region 818 is shown, in accordance withone embodiment of the present invention. The FIG. 8 diagram is presentedfor purposes of illustration, and in alternate embodiments, keypoints814 and support regions 818 may be implemented using various elementsand configurations in addition to, or instead of, certain of thoseelements and configurations discussed in conjunction with the FIG. 8embodiment.

In the FIG. 8 embodiment, a test image 324 is shown with an exemplarykeypoint 814 surrounded by a support region 818 with a given localorientation 822 that is represented by a short line. For purposes ofclarity, test image 324 is shown with only a single keypoint 814.However, in practice, test image 424 typically would have many keypoints814 at locations determined by keypoint generator 412 (FIG. 4).

As discussed above in conjunction with FIG. 7, a support region filter416 (FIG. 4) may perform a support region filtering procedure byutilizing any effective techniques. For example, in certain embodiments,support region filter 416 may accept only support regions 818 that arelarger than a predefined support-region size threshold. In the FIG. 8embodiment, the support-region size threshold may be set at nine pixels.In addition, support region filter 416 may reject any highly-overlappedsupport regions during the support region filtering procedure.

Referring now to FIGS. 9A and 9B, diagrams illustrating an orientationfiltering procedure are shown, in accordance with one embodiment of thepresent invention. The FIG. 9 diagrams are presented for purposes ofillustration, and in alternate embodiments, orientation filteringprocedures may be implemented using elements and techniques in additionto, or instead of, certain of those elements and techniques discussed inconjunction with the FIG. 89 embodiment.

In accordance with the present invention, true matching regions are manyin number and coherent in principle orientation. During the orientationfiltering procedures, orientation filter 420 evaluates the relativeorientations of corresponding pairs of keypoints from a test image 324(FIG. 7) and a database image 540 (FIG. 5). In the FIG. 9A embodiment,an orientation 822(a) of a keypoint/support region in a test image isshown. Similarly, FIG. 9A shows an orientation 822(b) of akeypoint/support region in a database image. In certain embodiments,orientation filter 420 may compare the orientations 822(a) and 822(b) toderive an orientation difference δθ, as shown in FIG. 9A.

FIG. 9B shows a set of keypoints 914 from a test image connected bycandidate lines to corresponding keypoints 918 from a database image.Orientation filter 420 may perform the orientation filtering procedureby utilizing any effective techniques. For example, in certainembodiments, orientation filter 420 may require the number of candidatelines between corresponding keypoints to be greater than a predeterminedcandidate threshold. In certain embodiments, the candidate threshold maybe set at ten candidate lines.

In certain embodiments, orientation filter 420 may require that thestandard deviation σ(δθ) of the orientation difference δθ for matchedcandidate lines satisfy the following equation: σ(δθ)<π/3. During theorientation filtering procedure, the orientation filter 420 may alsorequire that the individual orientation difference is within σ(δθ) ofthe mean value of all the candidate lines. The FIG. 9B example showsfive pairs of keypoints. Four of the pairs have relatively similarorientations, and therefore they would be accepted. However, one outlier922 is shown with a significantly different orientation. Accordingly,orientation filter 420 would remove, keypoints of the outlier 922 fromfurther consideration during the orientation filtering procedure.

The invention has been explained above with reference to certainembodiments. Other embodiments will be apparent to those skilled in theart in light of this disclosure. For example, the present invention mayreadily be implemented using configurations and techniques other thanthose described in the embodiments above. Additionally, the presentinvention may effectively be used in conjunction with systems other thanthose described above. Therefore, these and other variations upon thediscussed embodiments are intended to be covered by the presentinvention, which is limited only by the appended claims.

What is claimed is:
 1. A system for performing an image categorizationprocedure, comprising: an image manager that computes descriptors forkeypoints in a test image, said image manager filtering said descriptorsto produce filtered descriptors, said image manager comparing saidfiltered descriptors to one or more image sets for categorizing saidtest image; and a processor of an electronic device that controls saidimage manager to perform said image categorization procedure.
 2. Thesystem of claim 1 wherein said image manager performs a down-samplingprocedure on said test image during said image categorization procedure.3. The system of claim 1 wherein said image manager performs, a spectralfiltering procedure on said test image during said image categorizationprocedure to remove lower frequency characteristics.
 4. The system ofclaim 1 wherein said test image and one of said image sets share a highproportion of content correlation and have limited changes in imagescharacteristics including composition, content, exposure, and camerasettings including zoom, shift, and rotation.
 5. The system of claim 1wherein said image manager utilizes a keypoint generator to identifykeypoints in said test image according to predefined keypoint criteria.6. The system of claim 5 wherein said keypoint generator identifiesspecific horizontal and vertical coordinates (x, y) for each of saidkeypoints.
 7. The system of claim 6 wherein said keypoint generatorderives support regions σ corresponding to each of said keypoints. 8.The system of claim 7 wherein said keypoint generator defines specificorientations θ for each of said keypoints.
 9. The system of claim 8wherein said keypoint generator utilizes said, horizontal and verticalcoordinates, said support regions, and said orientations to createinitial descriptors S (x, y, σ, θ) for each of said keypoints.
 10. Thesystem of claim 9 wherein said initial descriptors are implemented asmulti-dimensional descriptors with 128 dimensions.
 11. The system ofclaim 9 wherein said image manager utilizes a support region filter toperform a support region filtering procedure upon said initialdescriptors to produce refined descriptors.
 12. The system of claim 11wherein said support region filter accepts only support regions that arelarger than a predefined support-region size threshold.
 13. The systemof claim 11 wherein said support region filter rejects anyhighly-overlapped ones of said support regions during said supportregion filtering procedure.
 14. The system of claim 11 wherein saidimage manager utilizes an orientation filter to perform an orientationfiltering procedure upon said refined descriptors to produce filtereddescriptors.
 15. The system of claim 14 wherein said orientation filterremoves outliers that include pairs of said keypoints from said testimage and said image set that fail to conform to predefined orientationlimitations.
 16. The system of claim 15 wherein said orientation, filterrequires that a standard deviation σ(δθ) of an orientation difference δθsatisfy an equation:σ(δθ)<π/3
 17. The system of claim 15, wherein said orientation filterrequires that an individual orientation difference be within saidstandard deviation σ(δθ) of a mean value of all orientation linesbetween said pairs of said keypoints.
 18. The system of claim 14 whereinsaid image manager utilizes a matching module to perform a matchingprocedure by comparing said filtered descriptors to calculateddescriptors of categorized images from said image sets that are storedin a database.
 19. The system of claim 18 wherein said matching modulecalculates a Euclidean metric to define a matching distance d(D_(i),D_(i)′) between each corresponding pair of said keypoints by comparingdescriptors from said test image and said categorized images, saidmatching module identifying a matching result if a total number ofmatching descriptors is greater than a predefined match threshold, saidmatching module also perform a match filtering procedure to reject falsepositive matches by requiring a low matching distance d(D_(i), D_(i)′)that is less than a predefined distance threshold.
 20. A method forperforming an image categorization procedure, comprising the steps, of:computing descriptors for keypoints in a test image; filtering saiddescriptors to produce filtered descriptors; and comparing said filtereddescriptors to one or more image sets for categorizing said test image.